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Data Handling and Statistics 3 is the third applied statistics units offered by the School of Natural Sciences (Mathematics). It provides an extension of the concepts, methods and tools introduced in KMA253. It is a 'hands-on' course in which the emphasis is on the development of skills in the selection and application of upper-level statistical methodology. Emphasis is also placed on the presentation of statistical analyses in a written format that promotes reproducible research. Topics covered in the course include: hypothesis testing, experimental design, inference, analysis presentation, generalised linear modelling; mixed-effects modelling, multinomial regression, and model selection. Expertise with the statistical computing language R and RStudio will be extended, including the application of R Markdown for promoting reproducible research. Examples will be drawn from the biological, physical and social sciences.

Summary 2020

Unit name Data Handling and Statistics 3
Unit code KMA353
Credit points 12.5
Faculty/School College of Sciences and Engineering
School of Natural Sciences
Discipline Mathematics

Teaching staff

Shane Richards

Level Advanced
Available as student elective? Yes
Breadth Unit? No



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Teaching Pattern

3 x 1hr face-to-face lectures, 1x1hr tutorial, 1x1-hr computer lab sessions.


3 class tests (46%), projects (54%)

TimetableView the lecture timetable | View the full unit timetable




Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, and Pius Korner-Nievergelt (2015) Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press.

Claus Thorn Ekstrom (2017) The R primer. Second edition. CRC Press.

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